Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
Fast planning through planning graph analysis
Artificial Intelligence
Discovering State Constraints in DISCOPLAN: Some New Results
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Planning as Heuristic Search: New Results
ECP '99 Proceedings of the 5th European Conference on Planning: Recent Advances in AI Planning
New admissible heuristics for domain-independent planning
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
PDDL2.1: an extension to PDDL for expressing temporal planning domains
Journal of Artificial Intelligence Research
The automatic inference of state invariants in TIM
Journal of Artificial Intelligence Research
STRIPS: a new approach to the application of theorem proving to problem solving
IJCAI'71 Proceedings of the 2nd international joint conference on Artificial intelligence
Local search topology in planning benchmarks: an empirical analysis
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
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Heuristic search has been widely applied to classical planning and has proven its efficiency. Even GraphPlan can be interpreted as a heuristic planner. Good heuristics can generally be computed by solving a relaxed problem, but it may be difficult to take into account enough constraints with a fast computation method: The relaxed problem should not make too strong assumptions about the independence of subgoals. Starting from the idea that state-of-the-art heuristics suffer from the difficulty to take some interactions into account, we propose a new approach to control the amount and nature of the constraints taken into account during a reachability analysis process. We formalize search space splitting as a general framework allowing to neglect or take into account a controlled amount of dependences between sub-sets of the reachable space. We show how this reachability analysis can be used to compute a range of heuristics. Experiments are presented and discussed.